Though the temperatures might not always feel it, at least in Philadelphia, summer is ending and autumn beginning. Consequently I wanted to share this neat little work that explores urban heat islands. Specifically, this post’s author looks at Massachusetts and starts with a screenshot of the Boston area.
The author points out that the Boston Common and Public Garden are two areas of cool in an otherwise hot Boston. He also points out the Charles River and the divide between Boston and Brookline. I would like to add to it and point out the Fens and the Emerald Necklace.
I wonder if a scale of sorts would help, though the shift from warm yellows and reds to cooler greens and blues certainly helps differentiate between the cooler and warmer areas.
I mean come on, guys, did you really expect me to not touch this one?
Well we made it to Friday, and naturally in the not so serious we have to cover the sharpie map. Because, if the data does not agree with your opinions, clearly the correct response is to just make shit up.
By now you have probably all heard the story about how President Trump tweeted an incorrect forecast about the path of Hurricane Dorian, warning how Alabama could be “hit (much) harder than anticipated”. Except that the forecast at the time was that Alabama wasn’t going to be hit. Cue this map, days later. As in days. As in this news story continued for days.
So to be fair, I went to the NOAA website and pulled down from their archive the cone maps from the date of the graphic Trump edited, and the one from the day when he tweeted about Alabama being hit by the hurricane.
Important to note that this forecast dates from 29 August. This press conference was on 4 September. He tweeted on 1 September. So in other words, two days after he used the wrong forecast, he had printed a big version of a contemporaneously two-day old forecast to show that if he drew a sharpie line on it, it would be correct.
Here is the original, from the National Hurricane Centre, for 29 August. Note, no Alabama.
And then Trump tweeted on 1 September. So let’s take the 02.00 Eastern time 1 September forecast from NOAA.
Definitely no Alabama in that forecast.
This could have all gone away if he had just admitted he looked at the wrong forecast and tweeted an incorrect warning. Instead, we had the White House pressuring NOAA to “fix” their tweet.
Now we can all chalk this up as funny. But it does have some serious consequences. Instead of people in the actual path of Dorian preparing, because of the falsely wide range of impacts the president suggested, people in Alabama needlessly prepared for a nonevent.
But more widely, as someone who works with data on a daily basis, we need to agree that data is real. We cannot simply change the data because it does not fit the story we want to tell. If I could take a screenshot of every forecast and string them together in an animated clip, you would see there was never any forecast like the sharpie forecast. We cannot simply create our own realities and choose to live within them, because that means we have no common basis on which to disagree policy decisions that will have real world impacts.
Credit for the photo goes to Evan Vucci of the AP.
Credit for the National Hurricane Centre maps goes to its graphic team.
For all my American readers, I hope you all enjoyed their Labour Day holiday. For the rest of you, today is just a Tuesday. Unless you live in the Bahamas, then today is just another nightmarish day as Hurricane Dorian continues his assault on the islands.
The storm will be one for the record books when all is said and done, and not just because of the damage likely to be catastrophic when people can finally emerge and examine what remains. The storm, by several metrics, is one of the most powerful in the Atlantic since we started recording data on hurricanes. If we look at pressure and sustained wind speeds, i.e. not wind gusts, Sam Lillo has plotted the path of Dorian through those metrics and found it sitting scarily in the lower-right corner of this plot.
The graphic does a couple of nice things here. I like the use of colour to indicate the total number of observations in that area. Clearly, we see a lot more of the weaker, higher pressure storms. Hence the dark blue in the upper-left. But then against that we have the star of the graphic, and my favourite part of the plot: the plot over time of Dorian’s progress and intensification as a storm. The final green dot indicates the point of the last observation when the graphic was made.
Overall this is a simple and solid piece that shows in the available historical context just how powerful Dorian is. Unfortunately that correlates with likely heavy damage to the Bahamas.
Credit for the piece I presume goes to Sam Lillo, though with the Twitter one can never be entirely certain.
Yesterday we looked at Billy Penn’s graphics about the cooler stations and I mentioned a few ways the graphic could be improved. So last night I created a graphic where I explored the limited scope of the data, but also showing how low the temperatures were, relative to the air temperature outside, using weather data from the National Weather Service, admittedly from Philadelphia International Airport, not quite Centre City, which I would expect to be warmer due to the urban heat bubble effect.
I opted to exclude the Patco Line since the original dataset did not include it either. However a section of it does run through Centre City and could be relevant.
Credit for the piece goes to me, though the data is all from Billy Penn and the National Weather Service.
Those of you living on the East Coast, specifically the Mid-Atlantic, know that presently the weather is quite warm outside. As in levels of dangerous heat and humidity. Personally, your author has not left his flat in a few days now because it is so bad.
Alas, not everyone has access to air conditioning in his or her abode. Consequently, they need to look to public spaces with air conditioning. Usually that means libraries or public buildings. But here in Philadelphia, have people considered the subway?
Billy Penn investigated the temperatures in Philadelphia’s subsurface stations along the Broad Street and Market–Frankford Lines—Philadelphia’s third and oft-forgot line, the Patco, was untested. What they found is that temperatures in the stations were significantly below the temperatures above ground. The Market–Frankford stations, for example, were less than 100ºF.
Of course that misses the 2nd Street station in Old City, but otherwise picks up all the Market–Frankford stations situated underground.
Then there is the Broad Street Line.
Here, I do have a question about why the line wasn’t investigated from north to south. It ran only as far north as Girard, stopping well short of north Philadelphia neighbourhoods, and then as far south as Snyder, missing both Oregon and Pattison (sorry, corporately branded AT&T) stations. The robustness of the dataset is a bit worrying.
The colours here too mean nothing. Instead blue is used for the blue-coloured Market–Frankford line and orange for the orange-coloured Broad Street line. (The Patco line would have been red.) Here was a missed opportunity to encode temperature data along the route.
Finally, if the sidewalk temperatures were measured at each station, I would want to see that data alongside and perhaps run some comparisons.
This is an interesting story, but some more exploration and visualisation of the data could have taken it to the next level.
Last week the Philadelphia area experienced a mini tornado outbreak with three straight days of watches and warnings. Of course further west in the traditional Tornado Alley, far more storms of far greater intensity were wreaking havoc. But with tornado warnings going off every few minutes just outside the city of Philadelphia, it was hard to concentrate on storms in, say, Oklahoma.
But the New York Times did. And they put together a nice graphic showing the timeline of the outbreak using small multiples to show where the tornado reports were located on 12 consecutive days.
Of course the day of that publication, 29 May, would see another few dozen, even in and around Philadelphia. Consequently, the graphic could have been extended to a day 13. But that would have been rather unlucky.
From a design standpoint, the really nice element of this graphic is that it works so well in black and white. The graphic serves as a reminder that good graphics need not be super colourful and flashy to have impact.
Credit for the piece goes to Weiyi Cai and Jason Kao.
Today’s piece is another piece set against a black background. Today we look at one on natural disasters, created by both weather and geography/geology alike.
The Washington Post mapped a number of different disaster types: flooding, temperature, fire, lightning, earthquakes, &c. and plotted them geographically. Pretty clear patterns emerge pretty quickly. I was torn between which screenshots to share, but ultimately I decided on this one of temperature. (The earthquake and volcano graphic was a very near second.)
It isn’t complicated. Colder temperatures are in a cool blue and warmer temperatures in a warm red. The brighter the respective colour, the more intense the extreme temperatures. As you all know, I am averse to warm weather and so I will naturally default to living somewhere in the upper Midwest or maybe Maine. It is pretty clear that I will not really countenance moving to the desert southwest or Texas. But places such as Philadelphia, New York, and Washington are squarely in the blacked out or at least very dark grey range of, not super bad.
It’s cold out in Chicago. And not just the usual winter cold, but record-setting cold. And when the temperature gets that low, when you mix in a little bit of wind, it can become dangerous very quickly. In an article about the weather conditions in the Midwest, the BBC included this graphic at the end.
Even the slightest bit of wind decreases the time one has before frostbite sets in. So wrap up and stay warm, everyone.
Credit for the piece goes to the BBC graphics department.
Christmas time is a time when people receive gifts. Well this year was no different and I received a few. One, however, was in a box stuffed with old newspaper pages. And it turns out one of said pages had a graphic on it. So let us spend today looking at this little blast from the past.
The piece looks at PECO outages, PECO being the Philadelphia region’s main electricity supplier. The article is full page and is both headed and footed with photography, the graphic in which we are interested sits centre stage in the middle of the page.
Overall the graphic is fairly compact and works well at showing the distribution of the outages, which the bar chart below the choropleth shows was historically significant. (Despite my years in Chicago, I was somehow in the area for all but the storm written about and can confirm that they were, in fact, disruptive.)
The choropleth works, but I question the colour scheme. The bins diverge at about 50%, which to my knowledge marks no special boundary other than “half”. If that yellow bin represented, say, the average number of outages per storm or the acceptable number of outages per storm, sure, I could buy it. Otherwise, this is really just degrees of severity along one particular axis. I would have either kept the bins all red or all blue and proceeded from a light of either to a dark of either.
I probably would have also dropped Philadelphia entirely from the map, but I can understand how it may be important to geographically anchor readers in the most populous county to orientate themselves to a story about suburbia.
Lastly, I have one data question. With power lines down during an ice storm, I would be curious to see less of the important roadways as the map depicts and other variables. What about things like average temperature during the storm? Was the more urban and built-up Delaware County less susceptible because of an urban heat bubble preventing water from freezing? Or what about trees? Does the impact in the more rural areas have anything to do with increasing numbers of trees as one heads away from the city?
Those last data questions were definitely out of scope for the graphic, but I nevertheless remain curious. But then again, this piece is almost five years old. Just a look at how some graphical forms remain in use because of their solid ability to communicate data. Long live the bar chart. Long live the choropleth.
Credit for the piece goes to the Philadelphia Inquirer graphics department.
You may recall a few weeks ago there was a hurricane named Florence that slammed into the Carolina before stalling and dumping voluminous amounts of rain that inundated inland communities in addition to the damage by the storm surge in the coastal communities. At the time I wrote about a New York Times piece that explored housing density in coastal areas, specifically around the Florence impact area.
Well today the New York Times has a print graphic about something similar. It uses the same colours and styles, but swaps in a different data set and then uses a small multiple setup to include the Florida Panhandle. Of course the Florida Panhandle was just struck by Hurricane Michael, a Category 4 storm when it made landfall.
This one instead looks at median income per zip code to highlight the disparity between those living directly on the coast and those inland. In these two most recent landfall areas, the reader can clearly see that the zip codes along the coast have far greater incomes and, by proxy, wealth than those just a few zip codes further inland.
The problem is that rebuilding lives, communities, and infrastructure not only takes time, but also money. And with lower incomes, some of the hardest hit areas over the past several weeks could have a very difficult time recovering.
Regardless, the recoveries on the continental mainlands of the Carolinas and Florida will likely be far quicker and more comprehensive than they have been thus far for Puerto Rico.
The only downside with this graphic is the registration shift, which is why the graphic appears fuzzy as colours are ever so slightly offset whereas the single ink black text in the upper right looks clear and crisp.
Credit for the piece goes to the New York Times graphics department.